



'''simple docstring'''

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
			import datasets

class 							a__( snake_case__     ):






			@slow
			@require_torch
			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'					,   'prajjwal1/bert-tiny'						)
					snake_case__	     =BertTokenizer.from_pretrained('bert-base-uncased'						)

					snake_case__	     =bertabert.config.encoder.vocab_size
					snake_case__	     =tokenizer.sep_token_id
					snake_case__	     =tokenizer.cls_token_id
					snake_case__	     =128

					snake_case__	     =datasets.load_dataset('cnn_dailymail'					,   '3.0.0'					,   split='train[:1%]'						)
					snake_case__	     =datasets.load_dataset('cnn_dailymail'					,   '3.0.0'					,   split='validation[:1%]'						)

					snake_case__	     =train_dataset.select(range(32						)						)
					snake_case__	     =val_dataset.select(range(16						)						)

					snake_case__	     =4

					def _map_to_encoder_decoder_inputs(_UpperCAmelCase						):
							# Tokenizer will automatically set [BOS] <text> [EOS]
							snake_case__	     =tokenizer(batch['article']					,   padding='max_length'					,   truncation=_UpperCAmelCase					,   max_length=512						)
							snake_case__	     =tokenizer(batch['highlights']					,   padding='max_length'					,   truncation=_UpperCAmelCase					,   max_length=128						)
							snake_case__	     =inputs.input_ids
							snake_case__	     =inputs.attention_mask

							snake_case__	     =outputs.input_ids
							snake_case__	     =outputs.input_ids.copy()
							snake_case__	     =[
							    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
							]
							snake_case__	     =outputs.attention_mask

							assert all(len(_UpperCAmelCase						) == 512 for x in inputs.input_ids						)
							assert all(len(_UpperCAmelCase						) == 128 for x in outputs.input_ids						)

							return batch

					def _compute_metrics(_UpperCAmelCase						):
							snake_case__	     =pred.label_ids
							snake_case__	     =pred.predictions

							# all unnecessary tokens are removed
							snake_case__	     =tokenizer.batch_decode(_UpperCAmelCase					,   skip_special_tokens=_UpperCAmelCase						)
							snake_case__	     =tokenizer.batch_decode(_UpperCAmelCase					,   skip_special_tokens=_UpperCAmelCase						)

							snake_case__	     =sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

							return {"accuracy": accuracy}

					# map train dataset
					snake_case__	     =train_dataset.map(
					    _map_to_encoder_decoder_inputs					,   batched=_UpperCAmelCase					,   batch_size=_UpperCAmelCase					,   remove_columns=['article', 'highlights']					,   )
					train_dataset.set_format(
					    type='torch'					,   columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']					,   )

					# same for validation dataset
					snake_case__	     =val_dataset.map(
					    _map_to_encoder_decoder_inputs					,   batched=_UpperCAmelCase					,   batch_size=_UpperCAmelCase					,   remove_columns=['article', 'highlights']					,   )
					val_dataset.set_format(
					    type='torch'					,   columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']					,   )

					snake_case__	     =self.get_auto_remove_tmp_dir()

					snake_case__	     =SeqaSeqTrainingArguments(
					    output_dir=_UpperCAmelCase					,   per_device_train_batch_size=_UpperCAmelCase					,   per_device_eval_batch_size=_UpperCAmelCase					,   predict_with_generate=_UpperCAmelCase					,   evaluation_strategy='steps'					,   do_train=_UpperCAmelCase					,   do_eval=_UpperCAmelCase					,   warmup_steps=0					,   eval_steps=2					,   logging_steps=2					,   )

					# instantiate trainer
					snake_case__	     =SeqaSeqTrainer(
					    model=_UpperCAmelCase					,   args=_UpperCAmelCase					,   compute_metrics=_compute_metrics					,   train_dataset=_UpperCAmelCase					,   eval_dataset=_UpperCAmelCase					,   tokenizer=_UpperCAmelCase					,   )

					# start training
					trainer.train()









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




